Qwen2.5-VL Technical Report
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie
2025-02-20
Summary
This paper talks about Qwen2.5-VL, a new AI model that combines vision and language skills to understand images, videos, and text in a highly advanced way. It can do tasks like identifying objects in pictures, analyzing documents, and even understanding long videos with precise event tracking.
What's the problem?
Many AI models struggle to handle complex tasks that involve both visual and language understanding, such as analyzing charts or processing long videos. Traditional methods often rely on simplifying the data, which can lose important details, making these models less effective for real-world applications.
What's the solution?
The researchers created Qwen2.5-VL, which uses advanced techniques like dynamic resolution processing and absolute time encoding to handle images and videos in their original quality. It also includes a redesigned Vision Transformer (ViT) that reduces computational costs while maintaining high accuracy. This allows the model to excel in tasks like object localization, document parsing, and video analysis. It is also capable of acting as an interactive agent for real-world tasks like operating devices.
Why it matters?
This matters because Qwen2.5-VL sets a new standard for vision-language AI by combining high performance with efficiency. Its ability to process complex inputs like long videos or detailed documents makes it useful for industries like finance, education, and entertainment. By improving how AI handles multimodal tasks, this model could lead to smarter tools for analyzing data and solving problems in various professional fields.
Abstract
We introduce Qwen2.5-VL, the latest flagship model of Qwen vision-language series, which demonstrates significant advancements in both foundational capabilities and innovative functionalities. Qwen2.5-VL achieves a major leap forward in understanding and interacting with the world through enhanced visual recognition, precise object localization, robust document parsing, and long-video comprehension. A standout feature of Qwen2.5-VL is its ability to localize objects using bounding boxes or points accurately. It provides robust structured data extraction from invoices, forms, and tables, as well as detailed analysis of charts, diagrams, and layouts. To handle complex inputs, Qwen2.5-VL introduces dynamic resolution processing and absolute time encoding, enabling it to process images of varying sizes and videos of extended durations (up to hours) with second-level event localization. This allows the model to natively perceive spatial scales and temporal dynamics without relying on traditional normalization techniques. By training a native dynamic-resolution Vision Transformer (ViT) from scratch and incorporating Window Attention, we reduce computational overhead while maintaining native resolution. As a result, Qwen2.5-VL excels not only in static image and document understanding but also as an interactive visual agent capable of reasoning, tool usage, and task execution in real-world scenarios such as operating computers and mobile devices. Qwen2.5-VL is available in three sizes, addressing diverse use cases from edge AI to high-performance computing. The flagship Qwen2.5-VL-72B model matches state-of-the-art models like GPT-4o and Claude 3.5 Sonnet, particularly excelling in document and diagram understanding. Additionally, Qwen2.5-VL maintains robust linguistic performance, preserving the core language competencies of the Qwen2.5 LLM.